Africa’s statistical tragedy

Fifteen years ago, Easterly and Levine published “Africa’s Growth Tragedy”, highlighting the disappointing performance of Africa’s growth, and the toll it has taken on the poor. Since then, growth has picked up, averaging 5-6 percent a year, and poverty is declining at about one percentage point a year. The “statistical tragedy” is that we cannot be sure this is true.

Take economic growth, which is measured in terms of growth in GDP. GDP in turn is measured by national accounts. While there has been some progress, today, only 35 percent of Africa’s population lives in countries that use the 1993 UN System of National Accounts; the others use earlier systems, some dating back to the 1960s.

To show that this is not an arcane point, consider the case of Ghana, which decided to update its GDP last year to the 1993 system. When they did so, they found that their GDP was 62 percent higher than previously thought. Ghana’s per capita GDP is now over $1,000, making it a middle-income country.

The “tragedy” is that we were happily publishing GDP statistics and growth figures for Ghana over the last decades, when in fact the national accounts were understating GDP by 62 percent.

GDP statistics are actually not that bad when compared with poverty statistics. As said earlier, Africa’s poverty rate, defined as the percentage of people living on $1.25 a day, declined from 59 percent in 1995 to 50 percent in 2005. Economists have been patting themselves on the back for these results, because they confirm the basic principle that growth reduces poverty.

The situation becomes gloomier when we try to see what those percentages represent. The 2005 estimate, for instance, represents robust statistics for only 39 countries for whom we have internationally comparable estimates. And they are not even comparable over the same year. Only 11 African countries have comparable data for the same year. For the others, we need to extrapolate to 2005, sometimes (as in the case of Botswana) from as far back as 1993.

In short, even the economists’ celebratory estimate of poverty declining in Africa during a period of growth needs to be taken with a grain of salt. In reality, there are many countries for which we simply don’t know.What’s going on here?

The proximate causes of the problem with statistics are: weak capacity in countries to collect, manage and disseminate data; inadequate funding; diffuse responsibilities; and fragmentation, with many diffuse data collection efforts.

But I would submit that the underlying cause is that statistics are fundamentally political. Take the poverty estimates. They assess whether people are better off today than they were five years ago. If the estimate takes place during an election year, there is a strong tendency to keep the results under wraps. Worse still, there is a tendency to drag their feet in completing the survey. And the raw data of household surveys are almost never publicly available (so there is little chance of being able to replicate them).

There is another, equally political, aspect to the statistical tragedy. After a lot of bad experiences, the international community has decided that African countries should develop their own National Statistical Development Strategies (NSDS), and that all statistical activities should be consistent with the NSDS.

The tragedy is that donors, including the World Bank, undertake statistical activities without ensuring that they are consistent with the NSDS. Why? Because they need data for their own purpose—to publish reports—and this means getting it faster, with little time to strengthen the countries’ statistical capacity.

But just as Africans turned around their growth tragedy, they can turn around their statistical tragedy. By recognizing that the problem is political, we can attack it at its roots. Let me suggest three things. First, insist that all data be openly accessible and transparent. Kenya just did this (and so did Bangladesh's central bank). If these countries--not known for their strong governance performance--can do it, so can others.

Second, put in place standards akin to those with PRSPs, whereby all statistical activities have to be filtered through the NSDS. The NSDS should be reviewed at the highest level—analogously with the PRSP—and deviations from it should be reported at an equally high level.

And third, the behavior of donors with respect to statistics should be evaluated, much like the Center for Global Development’s commitment to development index, and made public.

Note: This is a summary of my keynote speech at the recent IARIW-SSA conference on "Measuring National Income, Wealth, Poverty and Inequality in African Countries." For a video, see below:

Comments

Very true. Not only are all statistics political they can also be ethnic. Ethnic groups in power are tempted to use statistics -- e.g national census figures -- to further their own aims. For instance, I know a country in Africa where the population density is said to increase the closer you get to the desert!

Amazing how studies can be confirmed?. The linkage between ecnomic growth and poverty is not very strong in Africa. The statistical problem is just one.
1. I strongly agree that our understading and measurement of these two phenomina is not clear and robust. How much production goes around in Africa that is not captured in the national accounts. How many financail transactions go through informal exchange without also being capture. If we contract so much loans, how much does this immedtaley affect our figures of GDP just the very minute these loans are recorded. GDP measurement of Economic growth is not complete in itself.
Measurement of poverty is even worse. From getting conceptaul clarity about poverty, being consistent in this definition, colleting and analyis of poverty data etc, the situation is unclear and lead to varying results. In the Gambia for example, poverty figures show wide disparity between poverty surveys as a results of inconsistencies in understanding and methodology. You cannot use the figures to carry out any meaningful analysis. The same too is true of countries like Bukina Faso where one study in the same year shows an increase in poverty when the other shows a decrease in poverty.
How then can you use two uncertaintities to establish a conclusion. therefore it is not only a tradegy but an unrelable situation. In my study of the relationship between economic growth and poverty in The Gambia, it was observed that with more than 30 years of economic growth, backed with poverty focussed national development plan, the poverty rate has increased. Why?
Beside the problem of statistics, the answers to this are many.
If i am sure of anything, it is this. That in a country like the Gambia, any increase in the performance of agric (eg. an increase in the share of Agric's contribution to GDP during periods of real GDP growth) leads to reduction in poverty level.
i think investment in Agriculture is the answer for Africa. It has been observed by other researchers that no coutry except for two has developed in the absence of Agricultural growth.
I think Mali is showing the seeds

Well the real question is if Africa is getting richer, then who is getting richer? There is no point in us being pleased if its the klepotracy getting richer or even those with cash already. Are the poor genuinely getting richer?
I agree with one of the last statements. Investment is needed but it must be African if small scale and/or genuine (ie. not exploitative) if on a larger scale. Time for the WB to put its money where its mouth is.

Thanks for bringing much needed attention to issue of statistical capacity in Africa and to the need for a sustained and systematic approach to addressing the problem. Your suggestions - that available data be open and accessible, that statistical development be organized around a country's own plans (the NSDS), and that statistical activities sponsored by donors support improvements in capacity rather than -- in some cases -- reducing capacity, are all important.
But your post overlooks much of the work that's been achieved by African countries in the past few years. For instance, while survey data in some countries may still not be easily accessible, many countries are starting to make real progress on that. Here's one, Tanzania: http://www.nbs.go.tz/tnada/index.php/catalog/. That's an outcome of the efforts of countries and development partners working together through the PARIS21 partnership (http://www.paris21.org) -- in this case, the Accelerated Data Program. And of course it's because of the efforts of countries and partners working together through PARIS21 that most countries now do have NSDSs from which they can build.
In my view what's needed now is an effort to sustain investment in the systems that
form the basis for sound statistics, and which developed countries take for granted. For instance, very few countries in Africa have vital registration systems that can be used to monitor demographic events and change -- cause of death is particularly difficult of course. And all too few countries have household survey programs that are financed on a regular basis. It's the lack of these basic systems that can cause donors and development partners to behave as they do, and which creates all sorts of perverse institutional incentives that tend to work against the development of sustainable capacity.
After this I'm sure you will be fully behind the call for a renewed action plan to address these and other statistical capacity issues at the upcoming High Level Forum on Aid Effectiveness in Busan in November.
So that what we have is not the tragedy of African statistics, but the inevitable birth pains!

Shanta, really liked your video which confirms something I have been struggling with for a long time. Would it be fair to say that much of the poverty measures used for Africa in the WDI and thereby also the MDGs are in fact very unreliable to the point of being meaningless? When you delve into the data you find that a lot of them are based on household survey data which are done every ten years and as you say go back to the 1990s in some cases.
In work I have been doing, I have kept the decile distribution which comes out of the census work and in theory will not change too rapidly and then applied the consumption component of GDP to it, to get an up-to-date income distribution. This gives very different results from the MDG figures, even allowing for the PPP adjustment which I think the MDG uses. There are issues in oil countries but this could be controlled for with a bit of effort I think.
I have not been able to find anyone willing to discuss this in the WB, so very refreshing to hear this from you!
Best
Simon

I’m curious. Have you ever used PovcalNet (http://go.worldbank.org/WE8P1I8250) and read the background material provided there? I think you will concede that it is unfair, incorrect in fact, to say that “…poverty measures used for Africa… are in fact very unreliable to the point of being useless.” I am willing to discuss this further with you and I am sure that many other colleagues are too. A good starting point would be to share the work you have been doing.

Thank you for the response, and all the offline information you have provided. Apologies if my initial comment was a bit provocative! I agree that WB and others do the best possible with data available. I do also completely echo Shanta's points about limitations of the data because of timeliness and accuracy of collection or because of political interference. Collecting the data alone is a huge logistical task, especially in a continent which is still largely rural. Although there is nothing better available, it does not seem right that for example MDG1a is measured on data which for the most part for Africa is over 10 years old.

The statistical challenges (not tragedy) faced by some countries highlighted in this blog post are interesting conversation starters. But there are also African success stories in statistical capacity building. Commensurate efforts to reflect on these would be useful to inform the imperative scaling up of efforts to replicate these successes across the continent. The resources currently allocated by the Africa region of the Bank for strengthening statistical systems are a tiny fraction of our overall portfolio and are insufficient to meet the needs of our clients.
Regarding poverty measurement issues raised, the decline in Africa’s $1.25-a-day poverty rate estimates from 59 percent in 1996 (not 1995) to 50 percent in 2005 are based on survey data collected in countries mainly between 1995-1997 and 2004-2006; collectively these respectively covered 60% and 67% of the population in the region at the time. Thus the suggestion that extrapolation (based on national accounts growth data) over long periods of time was used extensively and undermined data quality are somewhat exaggerated. You can readily verify this from PovcalNet: the on-line tool for poverty measurement developed by the Bank’s Development Research Group (http://go.worldbank.org/WE8P1I8250). Extrapolation spanning longer time periods was only necessary for some countries, such Botswana (an upper-middle income economy with a population of less than 2 million which is home to less than 0.2 percent of the poor in the Africa region), for which the only household survey data made available to the Bank’s researchers who work on poverty measurement dates back to1993.
The problem is less with the data underlying poverty estimates up to 2005. Rather, the pertinent challenge is that 3 out of 4 countries in Sub-Saharan Africa have: (a) not collected any household survey data to measure poverty during the past 5 years, or (b) collected these data but either not yet processed these, not yet analyzed these, or not yet made these data available to the Bank’s research department to produce $-a-day estimates. A key challenge are the capacity constraints faced by many of our clients in Africa to process, analyze and provide access to recently collected household survey data in a timely fashion; for instance, as per the IMF’s General Data Dissemination Standard (GDDS) see: http://dsbb.imf.org/images/pdfs/gdds_oct_2003.pdf.
Botswana, for example, collected household income and expenditure survey data in 2002/2003 that could be used to improve recent $-a-day poverty estimates; but these have not been made available to the Bank. Another example is Nigeria, which collected a household living standards survey in 2008/2009, but has as of today not yet completed the processing of these data (i.e., transferred the information recorded on pencil and paper questionnaire forms into an electronic database) so that they can be analyzed. The Africa Chief Economist could use the influence and resources of his office to engage in a dialogue with our clients on such matters. Systematically providing support to clients who have already collected recent survey data or are in the process of doing so, but who face analytical or other capacity constraints would be one pragmatic step towards helping to improve the quality and timely dissemination of data available across the continent. I would submit that in many African countries the harsh reality of statistical capacity constraints, not statistical politics, is the fundamental problem that we need to tackle head on. Many countries in Latin America faced very similar challenges in the early 1990s, but today most have statistical systems in place that produce poverty estimates annually or every two to three years. Africa can learn from these successful statistical capacity building experiences too. Statistical politics is an unfortunate possibility only once statistical systems are strong but governance remains weak.

Glad someone like you have the words and the institutional clout to put this problem of statistics in the open. I was trained as an econometrician, did some of my immediate post Ph.D work trying to run some regressions. Luckily, I had the opportunity to work in in Ghana and soon realized to my chagrin that the fiscal and monetary data I was looking at could have been generated by a village watchman.
Imagine all the Africa regressions to prove the causality between export promotion and growth as if the logic was not obvious enough to our trained intuition. Even the census data and therefore the poverty reduction numbers leave so much to be diesired. The biggest joke of all, as you noted, is when by the stroke of a magical pen Ghana became a middle income country, much to the delight of politicians who wanted to justify Ghana's debt sustainability. The methodology of that exercise still remains a mystery. But the methodology should be an open source for researchers and policy analyst if we are to construct a consistent historical data for meaningful analysis.

Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination; that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination: that the statistician's task, in fact, is limited to the extraction of the whole of the available information on any particular issue.

It seems to me that development index, development indicators are not always reflect the realities on the grounds because they are sometime based only on global/regional /national statistical data which are limited in time and space and do not correlate with the realities on the ground (e.g. sociocultural infrastructure, human factors, internal and external cultural influences, etc.). In addition we can manipulate these data for individuals, group interests and/or business causes… This challenge was discussed in this publication (French edition)…

“Not everything that can be counted counts and not everything that counts can be counted” - Albert Einstein. - The limitations of quantitative /qualitative methods in the evaluation processes.